In our experiments, we choose three datasets designed to demonstrate the breadth of applications our model can be used to achieve successful segmentations. These datasets are a natural image dataset followed by two biomedical imaging datasets: retinal images and MRI images. These datasets contain images with different characteristics and the regions of interest are distinguished in different ways, and thus a model would need to be flexible to capture the meaningful axes of variation in each of these contexts. As previous deep learning segmentation methods are supervised, we compare to unsupervised methods that are not deep learning frameworks: an Otsu's method waterfall segmentation and Felzenszwalb segmentation  [1]}, [2]}. We utilize several different metrics to add robustness to our analysis, including those based on intrinsic similarities of the patches and those based on human annotations. In addition, we demonstrate that all aspects of our framework individually add value to the overall model by performing ablation studies of the different components.
